System and method for image calibration

ABSTRACT

The disclosure relates to a system and method for generating an image by the following steps: obtaining a first image and a second image relating to a subject; obtaining a third image of the subject by a radiology imaging technique; registering the first image and the second image to obtain a first element; calibrating the third image to obtain a second element based on the first element; calibrating the second image based on the second element.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/225,901, filed on Aug. 2, 2016, which claimspriority of Chinese Patent Application No. 201510528226.3 filed on Aug.25, 2015, and Chinese Patent Application No. 201610046405.8 filed onJan. 25, 2016, the contents of each of which are hereby incorporated byreference.

TECHNICAL FIELD

The present disclosure relates to an imaging processing, and moreparticularly, relates to a system and method for image calibration.

BACKGROUND

With the rapid developments of radiation-based imaging technologiesincluding, for example, computed tomography (CT), positron emissiontomography (PET), magnetic resonance imaging (MRI), and the expansion oftheir new clinical and industrial applications, the accuracy of thediagnosis may pose a challenge.

In one respect, a CT image and/or an MRI image may be combined with aPET image to provide a relatively thorough information of the testedsubject. Also, in another respect, methods and apparatus may be providedto compare results of two or more medical image scans acquired atdifferent time points, which may enable an accurate analysis of thechanges in, for example, a lesion area. Such methods and apparatus mayfacilitate the interpretation of the images, and increase the accuracyof the diagnosis.

SUMMARY

One aspect of the present disclosure relates to a method for imagecalibration. The method may include one or more of the followingoperations. A first image based on first data relating to a subject maybe obtained. A second image based on second data relating to the subjectmay be obtained. A third image of the subject relating to a radiologyimaging technique may also be obtained. The first image and the secondimage may be registered to obtain a first element relating to the firstimage and the second image. The third image may be calibrated based onthe first element to obtain a second element. The second image may becorrected based on the second element to generate a corrected secondimage.

According to some embodiments of the present disclosure, a plurality ofimages relating to the subject may be obtained. The first image and theplurality of images may be registered to obtain a plurality of firstelements. The third image may be calibrated based on the plurality ofthe first elements to obtain a plurality of second elements. Theplurality of images may be corrected based on the plurality of thesecond elements to generate a plurality of corrected images. In someembodiments, the plurality of images may include two to ten images. Insome embodiments, a region of interest may be identified in a scanningarea of the subject. A quantitative analysis may be further performed onthe region of interest, the results of which may be sequentiallydisplayed on a screen.

According to some embodiments of the present disclosure, the firstelement may be a motion field relating to the first image and the secondimage. The second element may include a CT image, an attenuationcorrection coefficient, or a scatter correction coefficient. In someembodiments, the first image may include a PET image corrected forattenuation correction based on the third image. In other embodiments,the first image may include a PET image without being corrected forattenuation correction. The registration relating to the first image andthe second image may be performed by applying an optical flow method formutual information maximization. The generation of the first image andthe second image may be based on scans of the subjects performed atdifferent time. The method may further include performing forwardprojection on the third image to obtain a third sinogram, based on whicha contour filter may be determined. The contour filter may be able tofilter the first image to generate filtered first data. And the firstimage may be reconstructed based on the filtered first data. The firstimage or the second image may include a PET image. The third image mayinclude a CT image. In some embodiments, the third image may be derivedfrom a correlation dictionary, which may include correlations between aplurality of MR images and a plurality of CT images. The CT image may beobtained by a sparse solution of the MR image of the subject and acorrelation corresponding to the MR image of the subject. Alternatively,the CT image may be obtained by combining CT sub-images. One of the CTsub-images may be obtained by a sparse of an MR sub-image and acorrelation corresponding to the MR sub-image. The method may furtherinclude generating an attenuation image based on the corrected secondimage. In some embodiments, the first image may be generated bycombining a plurality of first sub-images. The plurality of the firstsub-images may be generated from a plurality of sub-scans on thesubject.

One aspect of the present disclosure relates to a method for imagecalibration. The method may include one or more of the followingoperations. A first image based on first data relating to a subject maybe obtained. A third image of the subject may be obtained. A pluralityof iterations may be conducted. During each of the iterations, thefollowing operations may be performed. A second image relating to thesubject from a prior iteration may be obtained. The second image and thefirst image may be registered to obtain a first element. The third imagemay be calibrated based on the first element to obtain an attenuationcorrection coefficient. A scatter correction coefficient may be obtainedbased on the attenuation correction coefficient, and an updated secondimage may be generated by correcting the second image based on thescatter correction coefficient. A corrected second image may bereconstructed based on the scatter correction coefficient and theattenuation correction coefficient obtained in the last iteration of theplurality of iterations.

One aspect of the present disclosure relates to a system for imagecalibration. The system may include a data processing module and animage processing module. In some embodiment, the data processing modulemay be configured to obtain a first image based on first data relatingto the subject, a second image based on second data relating to thesubject, and a third image of the subject. The image processing modulemay be configured to register the first image and the second image toobtain the first element relating to the first image and the secondimage, calibrate the third image based on the first element to obtainthe second element, and correct the second image based on the secondelement to generate the corrected second image. The image processingmodule may be further configured to generate an attenuation correctioncoefficient based on the registration of the first image and the secondimage, and reconstruct the second image based on the attenuationcorrection and the second element. The system may further include acalculation unit configured to perform forward projection on the thirdimage to obtain a third sinogram, a filter configured to determine acontour filter based on the third sinogram and filter the first imagebased on the contour filter to obtain filtered first data, and an imagegenerator configured to reconstruct the first image based on thefiltered first data. In some embodiments, the system may include animage generator to generate the first image by combining a plurality ofsub-images. The plurality of sub-images may be generated by performing aplurality of sub-scans on the subject.

One aspect of the present disclosure relates to a system for imagecalibration. The system may include a data processing module and animage processing module. The data processing module may be configured toobtain a first image based on first data relating to the subject, and athird image of the subject. The image processing module may beconfigured to conduct a plurality of iterations. During each of theiterations, the following operations may be performed. A plurality ofsecond images relating to the subject from a prior iteration may beobtained. The plurality of second images and the first image may beregistered to obtain a plurality of first elements. The third image maybe calibrated based on the plurality of first elements to obtain aplurality of attenuation correction coefficients. A plurality of scattercorrection coefficients may be obtained based on the plurality ofattenuation correction coefficients, and a plurality of updated secondimages may be generated by correcting the plurality of second imagesbased on the plurality of scatter correction coefficients. A pluralityof corrected second images may be reconstructed based on the pluralityof scatter correction coefficients and the plurality of attenuationcorrection coefficients obtained in the last iteration of the pluralityof iterations. In some embodiments, one of the plurality of firstelements may be a motion field relating to the first image and one ofthe plurality of second images. The first image or the plurality ofsecond images may include a PET image. The third image may include a CTimage.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 illustrates an exemplary imaging system 100 according to someembodiments of the present disclosure;

FIG. 2 illustrates a block diagram of the image generation machine 160according to some embodiments of the present disclosure;

FIG. 3 illustrates a flowchart illustrating an image generation methodaccording to some embodiments of the present disclosure;

FIG. 4 illustrates an exemplary block diagram of a data acquisitionmodule according to some embodiments of the present disclosure;

FIG. 5 illustrates a flowchart illustrating a process for generating aPET image according to some embodiments of the present disclosure;

FIG. 6 is an exemplary flowchart illustrating a process for generating acalibrated PET image according to some embodiments of the presentdisclosure;

FIG. 7 is a flowchart illustrating a process for generating an estimatedCT image of a scanned subject from a corresponding MR image according tosome embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating a PET image generating process basedon a plurality of PET scans according to some embodiments of the presentdisclosure;

FIG. 9A illustrates a schematic diagram of a preliminary imageillustrating a process for generating PET sub-scanning areas accordingto some embodiments of the present disclosure;

FIG. 9B illustrates a schematic diagram illustrating a process forgenerating sub-scanning areas according to some embodiments of thepresent disclosure;

FIG. 10 illustrates a block diagram of a data processing module 220according to some embodiments of the present disclosure; and

FIG. 11 illustrates a flowchart illustrating a data processing methodaccording to some embodiments of the present disclosure.

FIG. 12A and FIG. 12B illustrate an exemplary CT image and acorresponding PET image, respectively, according to some embodiments ofthe present disclosure;

FIG. 12C illustrates an exemplary CT image calibrated by the motionfield determined by two PET images according to some embodiments of thepresent disclosure;

FIG. 12D and FIG. 12E illustrate an exemplary PET image and a calibratedPET image thereof according to some embodiments of the presentdisclosure;

FIG. 12F illustrates an exemplary motion-filed calibrated PET imagebased on the PET image illustrated in FIG. 12B according to someembodiments of the present disclosure;

FIG. 13A illustrates a PET image reconstructed from data acquired at afirst time point, without attenuation correction, according to someembodiments of the present disclosure;

FIG. 13B illustrates a PET image reconstructed from data acquired at asecond time point, without attenuation correction, according to someembodiments of the present disclosure;

FIG. 13C illustrates an attenuation image based on data acquired at thefirst time point according to some embodiments of the presentdisclosure;

FIG. 13D illustrates an estimated attenuation image corresponding to thesecond time point acquired by the methods described according to someembodiments of the present disclosure;

FIG. 13E illustrates a differential image between the estimatedattenuation image and an actual attenuation image from data acquired atthe second time point, according to some embodiments of the presentdisclosure; and

FIG. 13F illustrates a differential image between another estimatedattenuation image according to a traditional method and the actualattenuation image, according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of example in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to” or “coupled to” another unit,engine, module, or block, it may be directly on, connected or coupledto, or communicate with the other unit, engine, module, or block, or anintervening unit, engine, module, or block may be present, unless thecontext clearly indicates otherwise. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof. It willbe further understood that the terms “constructed” and “reconstruct”,when used in this disclosure, may represent a similar process that animage may be transformed from data.

FIG. 1 illustrates an exemplary imaging system 100 according to someembodiments of the present disclosure. As illustrated in FIG. 1, theimaging system 100 may comprise a scanning machine 170 and an imageprocessing machine 160. The scanning machine 170 may include a scanninggantry 110 and a couch 120. The image generation machine 160 may includean operation console 140 and an input-output interface 150. The imagegeneration machine 160 and the scanning machine 170 may beinterconnected via a wired or wireless link 130.

In some embodiments, the scanning gantry 110 of the scanning machine 170may be configured to scan a subject to be tested, and collect theprojection information related thereto. For example, the scanning gantry110 may detect radiation (e.g., X-ray) passing through the subject inthe case of a computed tomography (CT) system. As another example, thescanning gantry 110 may detect photons in a positron emission tomography(PET) system, or a single photon emission computed tomography (SPECT)system. As another example, the scanning gantry 100 may detect radiofrequency (RF) pulses in a magnetic resonance (MR) system. As stillanother example, the scanning gantry 110 may detect multiple signals ina multi-modality system. As used herein, the multi-modality system mayrepresent a combination of different imaging systems as described above.The operation of the scanning gantry 110 may be executed under thecontrol of the operation console 140.

The couch 120 may be configured to hold the subject and transport thesubject into the bore of the scanning gantry 110. In some embodiments,the couch 120 may be adjusted along the horizontal direction accordingto an organized time sequence to obtain scanning data of differentsections of the subject. In some embodiments, the couch 120 may beadjusted along the vertical direction manually or automatically.

The operation console 140 may be configured to control the scanninggantry 110 and the couch 120, and may regulate an image reconstructionprocess based on data collected through the scanning gantry 110 or othercomponents in the scanning 170 (e.g., another scanning gantry not shownin FIG. 1). Take the multi-modality system into account, the operationconsole may process data corresponding to different modalities (e.g., aCT system, a PET system) in a combined manner. For example, the datacorresponding to one modality may be used to calibrate the datacorresponding to another modality. In some embodiments, the operationconsole 140 may be configured to execute a command input by an operatorto adjust to the operation of the scanning machine 170.

The input-output interface 150 may be configured to receive data from anoperator and display information relating to an operation status to theoperator. Merely by way of example, the input-output interface 150 maydisplay a reconstructed image. In some embodiments, the input-outputinterface 150 may transmit the received information to the operationconsole 140.

The data collected by the scanning machine 170 may be transmitted to theimage generation machine 160 through the link 130. In some embodiments,the link 130 may take the form of a data line for communicating data. Insome embodiments, the link 130 may take the form of a telecommunicationsnetwork, a local area network (LAN), a wireless network, a wide areanetwork (WAN) such as the Internet, a peer-to-peer network, a cablenetwork, etc. The network may further connect to a remote server, acloud server, a specific database, or the like, for accessing datarelating to the image reconstruction process. Similarly, the imagegeneration machine 160 may send a command to the scanning machine 170through the link 130.

FIG. 2 is a block diagram of the image generation machine 160 accordingto some embodiments of the present disclosure. As shown in FIG. 2, theimage generation machine 160 may include a data acquisition module 210,a data processing module 220, an image processing module 230, a storagemodule 240, and a controller 250. The different modules may be connectedwith each other directly, or through an intermediate medium (not shown).In some embodiments, the intermediate medium may be a visible componentor an invisible field (radio, optical, sonic, electromagnetic induction,etc.). The connection between different modules may be wired orwireless. The wired connection may include using a metal cable, anoptical cable, a hybrid cable, an interface, or the like, or acombination thereof. The wireless connection may include using a LocalArea Network (LAN), a Wide Area Network (WAN), a Bluetooth, a ZigBee, aNear Field Communication (NFC), or the like, or a combination thereof.It should be noted that the above description about the imaging systemis merely an example, should not be understood as the only embodiment.Obviously, to those skilled in the art, after understanding the basicprinciples of the connection between different modules, the modules andconnection between the modules may be modified or varied withoutdeparting from the principles. The modifications and variations arestill within the scope of the current disclosure described above. Insome embodiments, these modules may be independent, and in someembodiments, part of the modules may be integrated into one module towork together.

The data acquisition module 210 may acquire imaging data from thescanning machine 170, and transmit the imaging data to the dataprocessing module 220. In some embodiments, the data acquired by thedata acquisition module 210 may be stored in the storage module 240. Thedata acquisition module 210 may acquire data under control of thecontroller 250. Merely by way of example, the controller 250 may controlthe time when the data acquisition module 210 may acquire data. Asanother example, the controller 250 may determine the data type (e.g. CTdata, PET data, MR data, etc.) corresponding to different modalitiesthat the data acquisition module 210 may acquire.

The data processing module 220 may process the imaging data relating tothe subject being tested. In some embodiments, the filtering process maybe performed on a set of PET data based on, for example, CT data and/orMR data. Details regarding the data processing module 220 may be foundin FIG. 10.

The image processing module 230 may reconstruct an image based on thedata transmitted from other modules. In some embodiments, the imageprocessing module 230 may reconstruct an image (e.g., a PET image, a CTimage) directly based on the data acquired by the data acquisitionmodule 310. In some embodiments, the image processing module 230 mayiteratively reconstruct an image based on intermediate data generatedby, for example, the image processing module 230. Merely by way ofexample, based on the acquired data, the image processing module 230 maygenerate multiple images, among which one image may be used to calibrateanother image. The calibrated image may be further processed to generatea final image.

The storage module 240 may be configured to store informationtransmitted by or processed by the data acquisition module 210, the dataprocessing module 220, the image processing module 230, or the like, ora combination thereof. The information may include programs, software,algorithms, data, text, number, images, voice, or the like, or acombination thereof. For example, a program for initiating a scan byinputting some initial parameters or conditions may be stored in thestorage module 240. Exemplary parameters or conditions may include thescanning time, the location of the subject for scanning, the rotatingspeed of the gantry, the sections of the subject for scanning, or thelike, or a combination thereof. As another example, some information maybe imported from an external resource, such as a floppy disk, a harddisk, a wireless terminal, or the like, or a combination thereof.

The controller 250 may be configured to control the scanning machine170, and other modules in the image generation machine 160. In someembodiments, the controller 250 may transmit commands to the imageprocessing module 230, the data processing module 220, the dataacquisition module 210, etc. Exemplary commands may include the datatype to be acquired and processed, the part of the image to bereconstructed, the iterative times, the algorithms occupied in thereconstruction, or the like, or a combination thereof. In someembodiments, the transmitted commands may be stored in the storagemodule 240, or an external storage to adjust some parameters, such as,the size of an image, the portion of a subject where image is to bedisplayed, or the duration that an image remains on a display screen.

FIG. 3 illustrates a flowchart illustrating an image generation methodaccording to some embodiments of the present disclosure.

In step 302, data relating to a subject may be acquired. In someembodiments, the subject may include a substance, a tissue, an organ, aspecimen, a body, or the like, or a combination thereof. In someembodiments, the subject may include a head, a breast, a lung, a pleura,a mediastinum, an abdomen, a long intestine, a small intestine, abladder, a gallbladder, a triple warmer, a pelvic cavity, a backbone,extremities, a skeleton, a blood vessel, or the like, or any combinationthereof.

In some embodiments, the data relating to the subject may be acquired byperforming a scan through a region of interest of a subject. In someembodiments, a number of protocols may be referred for scanningdifferent objects. Multiple parameters may be determined by theprotocols. Merely by way of example, the parameters may includecollimator aperture, detector aperture, X-ray tube voltage and/orcurrent, scan mode, table index speed, gantry speed, reconstruction FOV,or the like, or any combination thereof.

In step 304, the data acquired in step 302 may be processed. In someembodiments, the data may be pretreated before subsequent processes. Insome embodiments, the data may be filtered in order to accelerate thesubsequent processing or acquire a better reconstructed image. Forexample, In a PET-CT system, before reconstructing a PET image, the PETdata may be filtered based on the CT data.

In step 306, an image may be reconstructed based on the data processedin step 304 by the image processing module 230. In some embodiments,during the reconstruction, an image corresponding to one modality may bereconstructed to calibrate an image corresponding to another modality.Specifically, a PET image may be calibrated based on a CT image. In someembodiments, the reconstruction of an image may base on methodsincluding Fourier slice theorem, filtered back projection algorithm,iterative reconstruction, etc.

The image reconstructed in step 306 may be stored or output in step 308.The output image may undergo further process, such as noise reduction,contrast enhancement, etc., which may not be discussed in detailsherein.

It should be noted that the flowchart described above is provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,various variations and modifications may be conducted under the teachingof the present disclosure. However, those variations and modificationsmay not depart from the protecting of the present disclosure. Forexample, the step 304 may not be necessarily performed to reconstruct animage. As another example, the step 304 and the step 306 may beprocessed in an iterative manner such that imaging data transformed froma reconstructed image may be filtered for a subsequent reconstruction.

FIG. 4 illustrates an exemplary block diagram of a data acquisitionmodule according to some embodiments of the present disclosure. Asillustrated in FIG. 4, the data acquisition module 210 may include afirst data acquisition unit 410, a second data acquisition unit 420, anda third data acquisition unit 430. In some embodiments, the first dataacquisition unit 410, the second data acquisition unit 420, and thethird data acquisition unit 430 may be configured to acquire differenttypes of data corresponding to different imaging modalities. Forexample, the acquired data may include CT data, MR data, and/or PET datafrom the scanning machine 170. In some embodiments, the first dataacquisition unit 410 and/or the third data acquisition unit 430 may beconfigured to acquire PET-CT data from the scanning machine 170.Further, the first data acquisition unit, the second data acquisitionunit 420, and the third data acquisition unit 430 may operate separatelyor in a coordinated manner. Merely by way of example, only the firstdata acquisition unit 410 and the third data acquisition unit 430 maywork, when a subject may only be scanned for CT data and PET data.Likewise, only the second data acquisition unit 420 and the third dataacquisition unit 430 may work, when a subject may only be scanned for MRdata and PET data. Further, the first data acquisition unit 410, thesecond data acquisition unit 420, and the third data acquisition unit430 may operate concurrently or non-concurrently. In some embodiments,the first data acquisition unit 410 and the second data acquisition unit420 may acquire a set of CT data and MR data at a same time point, whilethe first data acquisition unit 430 may still acquire another set of PETdata at another time point.

FIG. 5 is a flowchart illustrating a process for generating a PET imageaccording to some embodiments of the present disclosure. In step 502, afirst PET image of a scanned subject may be obtained. In someembodiments, the image may be generated by performing a PET scan on asubject. For example, first PET data may be obtained by a PET scan, andthe first PET image may be reconstructed based on the first PET data. Insome embodiments, the first PET image may be obtained from an externalresource, such as a floppy disk, a hard disk, a terminal via a wiredconnection or a wireless connection, or the like, or a combinationthereof. In some embodiments, an attenuation correction may be performedon the first PET data before it being reconstructed to the first PETimage. In some embodiments, the first PET image may be obtained viaattenuation correction based on a CT image. The CT image used to conductattenuation correction on the first PET data or the first PET image maybe found in step 504. In some embodiments, the attenuation correction ofthe PET image may be a process of enhancing the image quality at asection of an image with a low image quality. The process may compensatethe energy level decay in the photons detected in the PET scan. In someembodiments, the PET scan may be implemented by the system 100. In someembodiments, the image may be obtained from a storage medium (e.g., thestorage module 240). In some embodiments, the image may be obtained froman external resource. In some embodiments, the external resource may beconnected to the system of the present disclosure through the link 130either via a wired connection or wirelessly and provide the PET image asneeded.

A first CT image of the subject may be obtained in step 504. In someembodiments, the first CT image may be obtained by performing aradiological scan of the subject. In some embodiments, the radiologicalscan may be a magnetic resonance imaging (MRI) scan or a computedtomography (CT) scan. Merely by way of example, the first CT image maybe generated based on an MR image by transforming the MR image into a CTimage. In some embodiments, the CT image and the first PET image may beobtained by performing two scans concurrently. Merely by way of example,the CT image and the first PET image may represent essentially the samescanning area of the scanned subject. As used herein, “essentially,” asin “essentially the same,” etc., with respect to a parameter or afeature may indicate that the variation is within 2%, or 5%, or 8%, or10%, or 15% of the parameter or the feature, or an average value of theparameter. Specifically, the CT image and the first PET image may begenerated by performing a PET-CT scan in, for example, the system 100.In some embodiments, the first CT image may be obtained from an externalresource.

In some embodiments, the first CT image may be obtained by at least someof the following steps. A database of CT image templates may beconstructed or otherwise provided. The CT image templates may be CTimages of a plurality of tissues and organs under different scanningconditions including, e.g., scanning areas and angles. An appropriateimage template may be selected from the CT image template database basedon one or more criteria including, for example, scanning area, angle, orthe like, or a combination thereof. Image registration may be performedbetween the selected image template and the first PET image obtained instep 502 to generate a first CT image. In some embodiments, theregistration may determine the relative displacement of pixels betweentwo images and may determine the correlation in size, shape, and/orposition of an identified tissue or organ based on the displacement ofpixels in one image relative to the other. The image registration may beconducted by the image processing module 230.

In step 506, a second PET image may be obtained. In some embodiments,the scanning area of the second PET scan may be the same as the scanningarea of the first PET scan. In some embodiments, the second PET imagemay be based on a scan of the subject performed at a different timepoint with respect to that of the first PET image. In some embodiments,second PET data may be obtained by a second PET scan. The second PETdata may be further reconstructed to provide the second PET image. Insome embodiments, a plurality of PET images (e.g. Img3, Img4, . . . ,ImgN) may be obtained in step 506 by performing a plurality of scans onthe subject. The scanning area of the plurality of PET scans (e.g. Img3,Img4, . . . , ImgN) may be essentially the same as the scanning area ofthe first PET scan. Further, the plurality of PET scans may be performedat different time points. In some embodiments, the second PET image maybe obtained according to maximum likelihood expectation maximization(MLEM) or maximum likelihood estimation of attenuation and activity(MLAA), based on second PET data. In some embodiments, the second PETimage may be acquired by ordered subset expectation maximization (OSEM).Merely by way of example, a PET image and an attenuation image may beiteratively updated in sub-iterations of the OSEM. In some embodiments,in a sub-iteration based on a subset of emission data, a PET image andan attenuation image may be updated according to, for example, formulas(12) to (15). As used herein, a PET image may be iterativelyreconstructed based on different subsets of emission data, among whichone sub-iteration may include reconstructing the PET image based on asubset of the emission data according to, for example, OSEM. In someembodiments, an attenuation image may be initialized, and during asub-iteration, the PET image and the attenuation image may be updatedsequentially. The sequential update may be performed by way of updatingthe PET image based on formula (13) while keeping the attenuation imageunchanged and updating the attenuation image based on formula (15). Insome embodiments, the initialization of the attenuation image may baseon the estimation of the second PET data. In some embodiments, theinitialization of the attenuation image may be set by default or by auser. In some embodiments, the initialization of the attenuation imagemay be performed by the image processing module 230.

After the second PET image is obtained, a registration may be performedbetween the first PET image and the second PET image to obtain a firstelement in step 508. In some embodiments, the first element may be amotion field between the first PET image and the second PET image. Insome embodiments, the motion field may be obtained by an optical flowmethod. In some embodiments, the motion field may be used to calibratethe first PET image to generate a motion-filed calibrated PET image. Themotion-field calibrated PET image may correspond to a calibrated secondPET image which in turn corresponds to the second PET image acquired byscanning in step 506. An exemplary motion-filed calibrated PET imagethat may be calibrated from the first PET image is illustrated in FIG.12F. In some embodiments, the registration may determine the relativedisplacements of pixels between two images in order to determine thechange in size, shape, and/or position of an identified section (e.g., atissue, an organ, etc.) based on the relative displacements of thepixels. In some embodiments, the registration may be performed by aregistration algorithm, based on, for example, mutual information. Insome further embodiments, conditioning that a plurality of PET images(e.g. Img3, Img4, . . . , ImgN) have been obtained in step 506, aplurality of the first elements may be obtained by registering the firstPET image and the plurality of PET images. Merely by way of example, Nmay range from 2 to 10.

After obtaining the first element, a second element may be obtainedbased on the first element and the first CT image in step 510. In someembodiments, the second element may be obtained by performing acalibration of the first CT image based on the first element. In someembodiments, the second element may be a calibrated second CT image. Insome embodiments, the second element may be an attenuation correctioncoefficient that may be used to correct the second PET image. In someembodiments, a plurality of first elements have been obtained in step508, and a plurality of the second elements may be obtained based on theplurality of first elements and the first CT image in step 510.

In step 512, the second element may be used to correct the second PETimage to generate a calibrated second PET image. In some embodiments,the second element may be used to perform an attenuation correction onthe second PET image. In some embodiments, the second element may beused to perform an attenuation correction on the data corresponding tothe second PET image. In some embodiments, a plurality of PET imageshave been obtained in step 506, a plurality of second elements have beenobtained in step 510, and a plurality of corrected images may begenerated by performing an attenuation correction on the plurality ofPET images based on the plurality of the second elements. In someembodiments, a scatter correction may be performed on the second PETimage Img2 to generated a scatter corrected second PET image based on,for example, a scatter correction coefficient. The scatter correctioncoefficient may be determined based on the attenuation correctioncoefficient and the first CT image.

After the second PET image is corrected, a judgment may be conductedwith respect to a condition in step 514. If the condition is satisfied,the process 500 may proceed to store and/or output the corrected secondPET image in step 516. If the condition is not satisfied, furtheriterations may be performed and the process 500 may proceed back to step506, in which the second PET image may be updated. There may bedifferent kinds of conditions to determine whether more iterations needto be performed. In some embodiments, the condition may relate to one ormore parameters set in the system. For example, the condition may bethat the difference between the reconstructed PET image from the currentiteration and the previous iteration is below a certain threshold. Insome embodiments, the condition may be that a certain number ofiterations have been performed. In some embodiments, the second PETimage in step 506 may be updated by the corrected second PET image fromthe previous iteration.

It shall be noticed that many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. In one example, thesequence in the flowchart may be adjusted that the first CT image may beobtained prior to the first PET image. Furthermore, the first PET imagemay be reconstructed from the first PET data corrected by the firstimage.

FIG. 6 is an exemplary flowchart illustrating a process for generating acorrected PET image according to some embodiments of the presentdisclosure. In step 602, a first PET image Img1 of a scanned subject maybe obtained. The first PET image Img1 may be obtained by methods asdescribed elsewhere in the disclosure. A first CT image of the scannedsubject may be obtained in step 604.

In some embodiments, the CT image may be obtained by at least some ofthe following steps. A database of CT image templates may be constructedor otherwise provide. The CT image templates may be CT images of aplurality of tissues and organs under different scanning conditionsincluding, e.g., scanning areas and angles. An appropriate imagetemplate may be selected from the CT image template database based onone or more criteria including, for example, scanning area, angle, orthe like, or a combination thereof. Image registration may be performedbetween the selected image template and the first PET image Img1obtained in step 602 to generate a first CT image. In some embodiments,the registration may determine the relative displacement of pixelsbetween two images and may determine the correlation in size, shape,and/or position of an identified tissue or organ based on thedisplacement of the pixels in one image relative to the other. In someembodiments, the first CT image may be obtained from an externalresource, such as a floppy disk, a hard disk, a wireless terminal, orthe like, or a combination thereof.

In some embodiments, the first CT image and the first PET image Img1 maybe obtained by performing two scans concurrently or sequentially. Insome embodiments, the first CT image and the first PET image Img1 may begenerated by performing a PET-CT scan. In some embodiments, the firstPET image Img1 may be corrected by way of attenuation correction basedon the first CT image.

In step 606, a second PET image Img2 of a scanned subject may beobtained. In some embodiments, the second PET image Img2 may be obtainedat a time different from the time the first PET image Img1 is obtained.

After obtaining the first PET image Img1 and the second PET image Img2,a registration may be performed between Img1 and Img2 in step 608 todetermine a motion field (also referred as a transformation field).

In some embodiments, the motion field may denote the displacement of apixel in an image compared to the pixel in another image. Forillustrative purposes, if the coordinate of a pixel in image A is (x, y,z), the motion field of the pixel is (Δx, Δy, Δz), and the image formedafter applying the motion field on image A is image B, the correspondingpixels in image B and image A may be expressed as:

B(x+Δx,y+Δy,z+Δz)=A(x,y,z),  (1)

In some embodiments, the registration between the first PET image Img1and the second PET image Img2 may be performed by a registrationalgorithm. In some embodiments, the registration algorithm may be basedon mutual information.

In some embodiments, registration of two images may be performed asfollows. An optimization function may be constructed. The optimizationfunction may be directly proportional to the degree of similaritybetween the images to be registered. One of the two images may beiteratively updated with respect to the motion field by optimizing thecost function in a neighborhood in order to achieve a higher degree ofsimilarity between the two images. In some embodiments, the optimizationfunction of mutual information may be expressed as:

$\begin{matrix}{{{I\left( {{T(X)},Y} \right)} = {{{H\left( {T(X)} \right)} + {H(Y)} - {H\left( {{T(X)},Y} \right)}} = {\sum\limits_{{x \in {T{(X)}}},{y \in Y}}{{p\left( {x,y} \right)}\log \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}}}}},} & (2)\end{matrix}$

where X and Y may denote two initial images, x and y may denote theintensity values of pixels in the images, T may denote a motion fieldthat may be applied on the image X, I may denote an optimizationfunction, H may denote an entropy, p(x, y) may denote the jointprobability when the pixel in X has a value x and the pixel in Y has avalue y, and p(x) and p(y) may denote the independent probability whenthe pixel in X is x and the pixel in Y is y, respectively.

In some embodiments, entropy H may be defined as:

H=−Σ _(i) p _(i) log p _(i),  (3)

where p_(i) may denote the probability of the value of the ith thepixel.

In some embodiments, motion field T may be defined as:

$\begin{matrix}{{T = {\max\limits_{T}{I\left( {{T(A)},B} \right)}}},} & (4)\end{matrix}$

In some embodiments, the motion field between the first PET image Img1and the second PET image Img2 may be obtained by an optical flow method.Specifically, the optical flow method may include obtaining a movingfeature of a moving object at different time points to determine achange of the moving object in a PET image. In some embodiments, the PETimage at different time points may be reconstructed and corrected basedon the change of the moving object.

After obtaining the motion field by performing a registration betweenImg1 and Img2, an attenuation correction coefficient U₂ and a scattercorrection coefficient S₂ may be obtained in step 610 based on themotion field and the first CT image. In some embodiments, the scattercorrection coefficient S₂ may be determined based on the attenuationcorrection coefficient U₂ and a specific model. For instance, the modelmay be a single scatter simulation (SSS) model. In some embodiments,after obtaining the attenuation correction coefficient U₂, random eventsand scatter events may be calculated for the reconstruction of PETimages. In some embodiments, the random events may be obtained byapplying a delay time window and the scatter events may be calculated byinitial chordal data and an attenuation image based on the SSS model. Asused herein, an attenuation image may be represented in the form of animage illustrating the attenuation of a radiation ray by a subject. Insome embodiments, scatter events may be predicted based on theattenuation image.

The SSS model may be expressed as:

$\begin{matrix}{{S^{AB} = {\int_{V_{s}}^{\;}{{{dV}_{s}\left( \frac{\sigma_{AS}\sigma_{BS}}{4\pi \; R_{AS}^{2}R_{BS}^{2}} \right)}\frac{\mu}{\sigma_{c}}\frac{d\; \sigma_{c}}{d\; \omega}\left( {I^{A} + I^{B}} \right)}}},} & (5) \\{{I^{A} = {ɛ_{AS}ɛ_{BS}^{\prime}e^{- {({{\int_{S}^{A}{\mu \; {ds}}} + {\int_{S}^{B}{\mu^{\prime}{ds}}}})}}{\int_{S}^{A}{\rho \; {ds}}}}},{and}} & (6) \\{{I^{B} = {ɛ_{AS}^{\prime}ɛ_{BS}e^{- {({{\int_{S}^{A}{{\mu \;}^{\prime}{ds}}} + {\int_{S}^{B}{\mu \; {ds}}}})}}{\int_{S}^{B}{\rho \; {ds}}}}},} & (7)\end{matrix}$

where (A, B) may denote a detector pair, S^(AB) may denote a count ratethat a photon pair in a single scattering is detected by the detectorpair, V_(s) may denote the volume of the scatter, S may denote ascattering point, σ_(AS) may denote a cross-section of AS line ofresponse with respect to detector A, σ_(AS) may denote a cross-sectionof BS line of response with respect to detector B, R_(AS) may denote thedistance between S and detector A, R_(BS) may denote the distancebetween S and detector B, μ may denote an attenuation coefficient withrespect to 511 KeV, μ′ may denote attenuation of a photon afterscattering, σ_(c) may denote a cross-section of a Compton contact, ω maydenote a scattering angle, ε_(AS) may denote the detection efficiency ofthe detector A at 511 keV, ε′_(AS) may denote the detection efficiencyof the detector A at an energy level corresponding to a photon afterscattering, ε_(BS) may denote the detection efficiency of the detector Bat 511 keV, ε′_(BS) may denote the detection efficiency of the detectorB at an energy level corresponding to a photon after scattering, and pmay denote a radioactivity distribution.

After obtaining the attenuation correction coefficient U₂ and thescatter correction coefficient S₂, the second PET image Img2 may becorrected in step 612 based on the scatter correction coefficient S₂. Insome embodiments, the Img2 may be reconstructed in an iterative manner.Merely by way of example, the reconstruction of Img2 may be performedaccording to maximum likelihood expectation maximization (MLEM), maximumlikelihood estimation of attenuation and activity (MLAA), or the like,or any combination thereof.

In some embodiments, the ordered subset expectation maximization may beexpressed as:

$\begin{matrix}{{x_{j}^{n,{m + 1}} = {\frac{x_{j}^{n,m}}{\sum\limits_{i}{A_{i}P_{ik}}}{\sum\limits_{i}{A_{i}P_{ik}\frac{y_{i}}{{A_{i}{\sum\limits_{k}{P_{ik}x_{k}^{n,m}}}} + r_{i} + s_{i}}}}}},} & (8)\end{matrix}$

where x_(j) ^(n,m) may denote an estimated value of the j_(th) pixel inthe reconstructed image after n times of iterations and m times ofsubset update, Σ_(i)A_(i)P_(ik) may denote a normalization coefficient,P_(ik) may denote a system response model, y_(i) may denote acquired PETsinogram, r_(i) may denote a correction term of random event, s_(i) maydenote scatter event, and A_(i) may denote attenuation coefficient.P_(ik), the system response model, may represent the probability that apixel unit count in the kth image is detected by the i_(th) line ofresponse. The relationship between the attenuation coefficient A_(i) andattenuation image μ may be expressed as:

A _(i) =e ^(−∫μ(x)dl),  (9)

where μ(x) may denote the coefficient of the x_(th) pixel in thegenerated attenuation image, and integration path l may move along thei_(th) line of response (LOR).

In some embodiments, a method for obtaining a scatter correctionestimate s_(i) (S₂ for the second PET image) may include: calculatingS^(AB); determining a ratio between S^(AB) and a smearing of theacquired chordal data outside the scanned subject, and multiplying theratio by S^(AB) to generate an estimated scatter correction estimates_(i).

In some embodiments, the second PET image and/or a subsequent PET imagemay be reconstructed iteratively according to formula (10) based on thefirst PET image Img1.

$\begin{matrix}{{x_{j}^{n,{m + 1}} = {\frac{x_{j}^{n,m}}{\sum\limits_{k}P_{ik}}{\sum\limits_{i}{P_{ik}\frac{y_{i}}{{\sum\limits_{k}{P_{ik}x_{k}^{n,m}}} + r_{i}}}}}},} & (10)\end{matrix}$

where y_(i) may denote the second PET image and/or a subsequentlyacquired PET chordal data.

The reconstruction of image may apply scatter correction, attenuationcorrection, or the combination thereof during an iteration processdepending on different conditions. In some embodiments, lack ofattenuation correction may result in a bright edge and a dark center inan image, and lack of scatter correction may result in a smearingartifact. In some embodiments, reconstructing the second PET image Img2without attenuation correction nor scatter correction may lead to boththe bright edge and the smearing artifact.

In some embodiments, the PET image may be reconstructed as:

$\begin{matrix}{{x_{j}^{n,{m + 1}} = {\frac{x_{j}^{n,m}}{\sum\limits_{k}P_{ik}}{\sum\limits_{i}{P_{ik}\frac{y_{i}}{{\sum\limits_{k}{P_{ik}x_{k}^{n,m}}} + r_{i} + s_{i}}}}}},} & (11)\end{matrix}$

where scatter correction may be applied without attenuation correctionon the reconstructed PET image.

In some embodiments, in order to have similar image quality, the firstPET image Img1 may also be constructed by formula (11).

After the second PET image Img2 is corrected, a judgment may beconducted with respect to a condition in step 614. If the condition issatisfied, the process may proceed to generate a final attenuationcorrection coefficient and a final scatter correction coefficient instep 616 based on the attenuation correction coefficient and the scattercorrection coefficient obtained in the current iteration. If thecondition is not satisfied, further iteration may be performed and theprocess may go back to step 606, in which the second PET image may beupdated. There may be different kinds of conditions to determine whethermore iterations need to be performed. In some embodiments, the conditionmay relate to one or more parameters set in the system, such as a numberof iterations, a requirement on the image quality of the constructedImg2, the difference between the constructed Img2 in successiveiterations, or the like, or any combination thereof. For example, thecondition may be that the difference between the reconstructed PET imagefrom the current iteration and the previous iteration is below a certainthreshold. As another example, the condition may be that a certainnumber of iterations have been performed. In some embodiments, theupdate of the second PET image may be performed based on the second PETimage obtained from the second PET scan and the reconstructed second PETimage from the previous iterations.

A final PET image Img2 _(final) may be generated in step 618 based onthe final attenuation correction coefficient and the final scattercorrection coefficient. In some embodiments, the final second PET imagemay be reconstructed by an ordered subset expectation maximization.

This description is intended to be illustrative, and not to limit thescope of the claims. Many alternatives, modifications, and variationswill be apparent to those skilled in the art. The features, structures,methods, and other characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. It should be appreciated forthose skilled in the art that a plurality of PET images (e.g. Img3,Img4, . . . , ImgN) may be obtained and corrected according to themethod of the present disclosure. Furthermore, the plurality of PETimages may be processed, such as displayed, based on the analysis on thePET images.

As described elsewhere in the disclosure, a CT image used in thereconstruction of a PET image may be obtained from a CT scan, or anothercorresponding scan (e.g., an MR scan). Merely by way of example, the CTimage generated may be the first CT image illustrated elsewhere in thepresent disclosure. Take MR scan as an example, FIG. 7 illustrates amethod of generating an estimated CT image of a scanned subject from acorresponding MR image according to some embodiments of the presentdisclosure.

In step 702, a plurality pairs of CT and MR images may be acquired. Insome embodiments, the pairs of images may be produced by performing a CTscan and an MR scan on a same subject. In some embodiments, the CT scanand the MR scan may be performed at essentially the same scanning areaand/or essentially the same angle. In some embodiments, the pairs ofimages may be obtained by a CT-MR system to acquire the CT and MR imagesconcurrently. In some embodiments, the plurality pairs of images maycorrespond to different subjects and/or different sections of thesubjects. In some embodiments, the sections may include but not limit toa head, a breast, a lung, a trachea, a pleura, a mediastinum, anabdomen, a long intestine, a small intestine, a bladder, a gallbladder,a triple warmer, a pelvic cavity, a backbone, extremities, a skeleton, ablood vessel, or the like, or any combination thereof.

After acquiring pairs of CT and MR images, a correlation dictionary maybe trained in step 704. In some embodiments, the correlation dictionarymay be trained by a support vector machine, a logistic regression, aback propagation neural network, an ordinary least square, a stepwiseregression, self-organizing map, a k-means algorithm, an expectationmaximization algorithm, or the like, or any combination thereof. In someembodiments, registrations (e.g., in geometry) may be performed betweenthe CT images and the MR images. Merely by way of example, registrationsbetween a CT image and an MR image may indicate the spatial difference(e.g., geometric difference) between the two images.

After being trained by a plurality of pairs of CT and MR images, (e.g.,correlations are formed between CT images and MR images), a trainedcorrelation dictionary may construct a projection relationship between aCT image and an MR image in step 706. In some embodiments, thecorrelation dictionary may generate estimated CT data in response to anMR image or MR data based on the projection relationship. Further, theestimated CT data may be reconstructed to provide an estimated CT image.In some embodiments, the correlation dictionary may generate anestimated MR image based on a CT image or CT data. In some embodiments,if an MR image and a CT image are both provided, an evaluating resultmay be generated based on the projection relationship. The evaluationresult may relate to whether the two images are obtained from a samesubject or essentially the same section of a subject. The correlationdictionary may be a knowledge graph, a neural network, a database, orthe like, or any combination thereof.

In step 708, an MR image of a scanned subject may be obtained. In someembodiments, the MR image may be generated by a direct MR scan on asubject. For example, MR data may be obtained by an MR scan, and the MRimage may be reconstructed based on the MR data. In some embodiments,the MR image may be obtained from a storage medium (e.g., the storagemodule 240). In some embodiments, the image may be obtained from anexternal resource. In some embodiments, the external resource may beconnected to the system of the present disclosure through the link 130either via a wired connection or wirelessly and provide the MR image asneeded.

In step 710, an estimated CT image may be generated based on the MRimage and the projection relationship. In some embodiments, by searchingthe correlation dictionary, a sparse solution corresponding to theprovided MR image may be obtained. Merely by way of example, the sparsesolution may be a vector or a matrix correlating the MR image with thecorrelation dictionary. Based on the sparse solution and the correlationdictionary, an estimated CT image may be generated in step 710. In someembodiments, the correlation dictionary may be provided by an externalresource, such as a floppy disk, a hard disk, a wireless terminal, orthe like, or a combination thereof. Merely by way of example, theestimated CT image may be the CT images used in the reconstruction ofthe PET images (e.g., the first PET image Img1, the second PET imageImg2) illustrated elsewhere in the present disclosure. In someembodiments, step 708 may include generating a plurality of MRsub-images based on the provided MR image. In some embodiments, theplurality of MR sub-images may be extracted from the provided MR images.Merely by way of example, the generation of the MR sub-images mayinclude grouping and/or identifying sections on the provided MR imagewith similar color or greyness. After the MR sub-images are generated,the sparse solutions to each sub-images may be obtained by searching thecorrelation dictionary. As a result, a plurality of correspondingestimated CT sub-images may be generated in step 710. Furthermore, theestimated CT sub-images may be combined to generate an estimated CTimage corresponding to the provided MR image.

In some embodiments, a sliding window may be used to generate aplurality of MR sub-images from the provided MR image, at least some ofwhich may at least partially overlap with each other. As a result, atleast some of a plurality of CT sub-images corresponding to theplurality of MR image blocks may overlap with each other. In someembodiments, the plurality of CT sub-images may have differentgreyness/color for pixels in the overlapping sections. Furthermore, thepixels with different greyness/color may be averaged and the CTsub-images may be combined to generate an estimated CT image.

This description is intended to be illustrative, and not to limit thescope of the claims. Many alternatives, modifications, and variationswill be apparent to those skilled in the art. The features, structures,methods, and other characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. It should be appreciated forthose skilled in the art that the image generating process disclosedherein may be used in generating a plurality types of images from othertypes. Merely by way of example, the images used for training mayinclude but not limit to a CT image, an MR image, a PET image, a SPECTimage, an ultrasound image, a non-radiological image, a camera photo orthe like, or any combination thereof.

FIG. 8 is a flowchart illustrating a PET image generating process basedon a plurality of PET scans according to some embodiments of the presentdisclosure. In step 802, a scanning area of a subject may be determined.In some embodiments, the step 802 may include performing a preliminaryscan to generate a preliminary image of the subject. The preliminaryscan may be a radiological or a non-radiological scan. In someembodiments, the preliminary image may provide information about thelocation, the appearance, and the shape of the scanned subject. In someembodiments, the user may select and/or determine a scanning area of thePET scan in the preliminary image, or further select an area of interestin the selected scanning area. In some embodiments, the area of interestmay correspond to an organ like heart, brain, or a tissue like muscle,skin, etc. In some embodiments, the determined scanning area may be inany regular or irregular shape.

After the scanning area and the area of interest are determined, aplurality of PET sub-scanning areas may be generated in step 804. Insome embodiments, the sub-scanning areas may correspond to suitablepositions of the PET detectors. In some embodiments, the sub-scanningareas, when combined, may cover the scanning area. In some embodiments,the sub-scanning areas may be combined to partly cover the scanningarea. Merely by way of example, the sub-scanning areas may be combinedto cover areas of interest of the subject. After obtaining the PETsub-scanning areas, a plurality of PET sub-scans may be performed on thesub-scanning areas to generate PET data in step 806. For example, thesub-scans may be performed by sequentially moving the couch through thePET detectors. In some embodiments, the PET data may be reconstructed toa PET image, such as, the first PET image in step 502, or the second PETimage in step 506.

In step 808, a CT image of the scanned subject may be obtained. In someembodiments, the CT image may be generated by a direct CT scan of thesubject. In some embodiments, the CT image may be estimated from an MRimage of the scanned subject. In some embodiments, the CT image may beobtained by registering a template CT image with a PET image of thesubject to generate an estimated CT image.

An attenuation image may be obtained based on the CT image in step 810.In some embodiments, the attenuation image may be generated byperforming a bilinear method on the CT image. As used herein, thebilinear method may include expressing a matter whose density is lowerthan water by a linear water-air model, and denoting a matter whosedensity is higher than water by a linear water-bone model. According todifferent models used for the matters, the attenuation coefficient ofgamma ray in different matters may be obtained to construct arelationship between the density distribution of matter and theattenuation coefficient. In some embodiments, according to the densitydistribution of the matter in the CT image and the relationship betweendensity and the attenuation coefficient, an attenuation image may beobtained. In some embodiments, an ordered subset method may be used toiteratively reconstruct the PET image based on the PET data and theattenuation image. During the iteration, a subsequent attenuation imagemay be updated by:

ā _(i) ^((n,m)) =e ^(−Σ) ^(j) ^(l) ^(ij) ^(u) ^(j) ^((n,m)) ,  (12)

where n may denote the number of iteration, m may denote the number ofsub-iteration in each iteration, and i may denote the sequence number ofthe line of response (LOR), ā_(i) ^((n,m)) may denote the attenuationcoefficient of the i_(th) element in a sinogram after n times ofiteration and m times of sub-iteration which is performed in the orderedsubset method, e may denote the base of natural logarithms, u_(j)^((n,m)) may denote the value of the jth voxel after n times ofiteration and m times of sub-iteration, and l_(ij) may denote a lineintegral matrix mapping the attenuation image with the attenuationcoefficient.

$\begin{matrix}{{f_{j}^{({n,{m + 1}})} = {\frac{f_{j}^{({n,m})}}{\sum\limits_{t,{i \in S_{m}}}{{\overset{\_}{a}}_{i}^{({n,m})}H_{ijt}}}{\sum\limits_{t,{i \in S_{m}}}{H_{ijt}\frac{1/ɛ_{i}}{{\sum\limits_{k,t}{H_{ikt}f_{k}^{({n,m})}}} + \frac{s_{i} + r_{i}}{{\overset{\_}{a}}_{i}^{({n,m})}}}}}}},} & (13)\end{matrix}$

where f_(j) ^((n,m+1)) may denote a PET image obtained after n times ofiteration and m times of sub-iteration, S_(m) may denote the m_(th)subset in data space, H_(ijt) and H_(ikt) may denote the transformationmatrix of the sinogram, k may denote the kth voxel in the PET image, tmay denote the sequence number of a time of flight box, ε_(i) may denotea standardized coefficient on data in a list, and s_(i) and r_(i) maydenote a number of scatter coincidence events and random coincidenceevents, respectively.

y _(i) ^((n,m+1)) =ā _(i) ^((n,m))Σ_(j,t) H _(ijt) f _(j)^((n,m+1)),  (14)

where y _(i) ^((n,m+1)) may denote the expectation value of the i_(th)voxel of the PET image after n times of iteration and m+1 times ofsub-iteration.

$\begin{matrix}{{\mu_{j}^{({n,{m + 1}})} = {\mu_{j}^{({n,m})} + \frac{\begin{matrix}{\sum\limits_{i \in S_{m}}{l_{ij}\frac{{\overset{\_}{y}}_{i}^{({n,{m + 1}})}}{{\overset{\_}{y}}_{i}^{({n,{m + 1}})} + s_{i} + r_{i}}\left( {{\overset{\_}{y}}_{i}^{({n,{m + 1}})} +} \right.}} \\{{\left. {s_{i} + r_{i} - y_{i}} \right) - {\beta \times \frac{\partial C}{\partial\mu}\left( {{\mu,\mu_{0}}} \right)}}}_{\mu = \mu^{({n,m})}}\end{matrix}}{{{{\sum\limits_{i \in S_{m}}{l_{ij}\frac{\left( {\overset{\_}{y}}_{i}^{({n,{m + 1}})} \right)^{2}}{{\overset{\_}{y}}_{i}^{({n,{m + 1}})} + s_{i} + r_{i}}{\sum\limits_{k}l_{ik}}}} + {\beta \times \frac{\partial^{2}C}{\partial\mu^{2}}\left( {{\mu,\mu_{0}}} \right)}}}_{\mu = \mu^{({n,m})}}}}},} & (15)\end{matrix}$

where μ_(j) ^((n,m+1)) may denote an attenuation image generated fromμ_(j) ^((n,m)) after n times of iteration and m times of sub-iteration,l_(ik) may denote a line integral matrix mapping the attenuation imagewith the attenuation coefficient, it may also denote a length of thei_(th) line of response (LOR) which crosses voxel k, y_(i) may denotethe number of annihilated photon pairs acquired from the i_(th) line ofresponse, βC(μ, μ₀) may denote a new penalty function,

$\frac{\partial C}{\partial\mu}\left( {{\mu,\mu_{0}}} \right)\mspace{14mu} {and}\mspace{14mu} \frac{\partial^{2}C}{\partial\mu^{2}}\left( {\mu,\mu_{0}} \right)$

may denote a first order derivative and a second order derivative ofβC(μ, μ₀) at μ=μ^((n,m)), and β may denote an adjustable penalty weight.A higher β may indicate that μ may have a higher chance to depart froman initial attenuation image μ₀ corresponding to the CT image. A penaltyfunction corresponding to formula (15) may be expressed as:

cost=Σ_(i)( y _(i) −y _(i) ln y _(i))+βC(μ,μ₀),  (16)

where “cost” may denote value of the penalty function, and Σ_(i)(y_(i)−y_(i) ln y _(i)) may denote an opposite number of a likelihoodfunction corresponding to statistical property of data.

In some embodiments, the cost may increase with the increase of Σ_(i)(y_(i)−y_(i) ln y _(i)) and the increase of βC(μ, μ₀) and may decreasewhen Σ_(i)(y _(i)−y_(i) ln y _(i)) and βC(μ, μ₀) decrease. In someembodiments, βC(μ, μ₀) may be configured to be proportional to thedifference between the initial attenuation image μ₀ and the attenuationimage μ generated from iterations. Merely by way of example, C(μ,μ₀)=|μ−μ₀|². In some embodiments, the “cost” may be reduced when thedifference between μ and μ₀ is reduced.

In some embodiments, the formulae (12)-(15) may be applied repeatedly toupdate the PET image and the attenuation image.

In some embodiments, one method of iterative reconstruction may includeiteratively updating a PET image based on formula (13) while keeping theattenuation image unchanged. In some embodiments, one method ofiterative reconstruction may include iteratively updating theattenuation image based on formula (15) while keeping the PET imageunchanged. An iteration may be terminated when all ordered subsets inthe previous iteration have been gone through. In some embodiments, theiterative reconstruction may terminate when a termination condition issatisfied to generate a final PET image and a final attenuation image.If the termination condition is not satisfied, the PET image and theattenuation image obtained in this iteration may be configured as theinitial images of next iteration and the iterative reconstructionprocess may repeat till the termination condition is satisfied.

In some embodiments, by reconstructing the PET image and the attenuationimage using an iterative method according to formulae (12) to (15), thePET image and the attenuation image may converge to the final imagesfaster than using a non-iterative method.

In some embodiments, the attenuation image may be updated according to:

$\begin{matrix}{{\mu_{j}^{({n,{m + 1}})} = {\mu_{j}^{({n,m})} + \frac{\sum\limits_{i}{l_{ij}\frac{{\overset{\_}{y}}_{i}^{(n)}}{{\overset{\_}{y}}_{i}^{(n)} + s_{i} + r_{i}}\left( {{\overset{\_}{y}}_{i}^{(n)} + s_{i} + r_{i} - y_{i}} \right)}}{\sum\limits_{i}{l_{ij}\frac{\left( {\overset{\_}{y}}_{i}^{(n)} \right)^{2}}{{\overset{\_}{y}}_{i}^{(n)} + s_{i} + r_{i}}{\sum\limits_{k}l_{ik}}}}}},} & (17)\end{matrix}$

In some embodiments, a penalty function corresponding to formula (17)may be expressed as

cost=−Σ_(i)( y _(i) −y _(i) ln y _(i)),  (18)

where “cost” may denote value of the penalty function, and Σ_(i)(y_(i)−y_(i) ln y _(i)) may denote an opposite number of a likelihoodfunction corresponding to statistical property of data.

In some embodiments, formula (17) may be generated by subtracting thefirst order derivative and second order derivative of new penaltyfunction βC(μ, μ₀) from the numerator and the denominator of formula(15) respectively. In some embodiments, the PET image and theattenuation image based on formula (15) may converge to same finalimages as formula (17).

In some embodiments, PET data or CT data may be obtained by performing aplurality of sub-scanning. Furthermore, a PET image or a CT image may bereconstructed from the obtained PET data or CT data. Merely by way ofexample, the first data relating to the first PET image, as illustratedelsewhere in the present disclosure, may be based on a first scan. Thefirst scan may include a plurality of first sub-scans, based on which aplurality of first sub-images may be generated. In some furtherembodiments, the first PET image may be generated by combining at leastsome of the first sub-images. Likewise, the second PET image may begenerated by performing a plurality of second sub-scans. In someembodiments, sub-scanning areas may be generated based on the size andshape of the scanning area. In some embodiments, the sub-scanning areasmay be combined to cover the scanning area. In some embodiments, thenumbers of sub-scanning areas may be determined by the axial length ofthe scanning area and the width of acquisition of the PET detector inaxial direction. In some embodiments, a method for determining thescanning area may include: configuring a box representing the scanningarea on the preliminary image; changing the size and shape of the boxaccording to the area of interest; configuring the final box as thescanning area of the subject. For instance, the box may have a shape ofa rectangle, a square, a parallelogram, a triangle, a trapezoid, apentagon, a hexagon, etc. In some embodiments, the shape and size of thebox may be changed by dragging at least one of the corners of the box.In some embodiments, the shape and size of the box may be changed bydragging at least one of the sides of the box. Details regardingobtaining a CT image from an MR image can be found in ChineseApplication 201410669318.9, the contents of which are herebyincorporated by reference.

FIG. 9A is a schematic diagram of a preliminary image illustrating aprocess of generating PET sub-scanning areas according to someembodiments of the present disclosure. As shown in FIG. 9A, image 901may be generated by a PET scan of a coronal section or a sagittalsection (also referred to as coronal scan and sagittal scanrespectively). Direction a (vertical direction in FIG. 9A) may be anaxial direction of the preliminary image. In some embodiments, the axialdirection may be along the long axis of the scanned subject. In someembodiments, the axial direction may be the longitudinal direction of ahuman body. Initially, a scanning area 902 may be determined based onthe preliminary image 901. Then, a plurality of sub-scanning areas 903may be determined. In some embodiments, the sub-scanning areas 903 maybe arranged along with direction a (also referred to as the axialdirection). In some embodiments, a sub-scanning area may correlate withthe width of a PET detector. In some embodiments, all sub-scanning areasmay have same or similar size and shapes. In some embodiments, thesub-scanning areas may be arranged so that a least one of thesub-scanning areas may overlap with one side (e.g., the upper boundaryin FIG. 9A) of the scanning area, and at least one of the sub-scanningareas may overlap with one opposite side (e.g., the lower boundary) ofthe scanning area. After the sub-scanning areas at two opposite sides ofthe scanning area is configured, the remaining scanning area may bedivided equally depending on the width of acquisition of the PETdetectors. In some embodiments, adjacent sub-scanning areas 903 mayoverlap with each other. In some embodiments, sub-scanning areas 903,when combined, may cover the scanning area 902.

In some embodiments, a method of generating sub-scanning areas 903 basedon the scanning area 902 may include: determining the number ofsub-scanning areas needed to cover the scanning area according to theaxial length of the sub-scanning areas (also referred to as the width ofacquisition of PET detector) and the axial length of overlapping areasbetween sub-scanning areas; determining the actual axial length ofoverlapping areas based on the determined number of sub-scanning areas;and adjusting the axial length of overlapping areas as needed. If theaxial length of overlapping areas cannot be greater than the value asneeded after every possible adjustment, the number of sub-scanning areasmay be increased. Merely by way of example, the axial length of ascanning area is 1.2 meters, the axial length of PET sub-scanning areasis 0.4 meters, and the minimum axial length of the overlapping areas is8 centimeters. At first, the smallest number of sub-scanning areas maybe determined as four and the axial length of overlapping areas may becalculated as 13.3 centimeters. A judgment may then be performed tocheck whether 13.3 centimeters is greater than the minimum axial length,which is 8 centimeters. As 13.3 centimeters is actually greater than 8centimeters, one of the possible arrangement of the axial length of theoverlapping areas between four sub-scanning areas may be 13.3centimeters, 13.3 centimeters and 13.3 centimeters. However, if theareas of interest according to some embodiments of the presentdisclosure are configured to have a particular axial ranges, thearrangement of the axial length of the overlapping areas between foursub-scanning areas may be 8 centimeters, 24 centimeters, and 8centimeters.

This description is intended to be illustrative, and not to limit thescope of the claims. Many alternatives, modifications, and variationswill be apparent to those skilled in the art. The features, structures,methods, and other characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. It should be appreciated forthose skilled in the art that any arrangement of the axial length ofoverlapping areas between sub-scanning areas may be reasonable as longas the axial length is greater than the minimum requirement.

FIG. 9B is a schematic diagram illustrating a process of generatingsub-scanning areas. Similar to FIG. 9A, area 906 may be a scanning area,area 907 may be a sub-scanning area, and area 908 may be an overlappingarea between two sub-scanning areas. The axial length of thesub-scanning area 903 may be L, the axial length of the overlapping area908 may be D, and the line graph 905 may represent an image quality atdifferent axial values, where direction b may represent an increase ofthe image quality.

In some embodiments, for each of the sub-scanning areas 903, centralsections may correspond to highest image quality as shown in the linegraph 905. In some embodiments, the axial length of the overlapping areaD may be 0.15 to 0.5 times of the axial length of the sub-scanning areaL. It can be seen that the image quality at the edge of eachsub-scanning area is enhanced due to the overlapping of neighboringsub-scanning areas.

In some embodiments, there may be a plurality of areas of interest inthe scanning area, which may need to have a high image quality. In someembodiments, the scanning area 902 may be moved along the axialdirection to ensure the important areas are inside or approaching anarea with high image quality. For example, important areas may bearranged at a central section of a sub-scanning area. As anotherexample, the image quality of an area of interest may be enhanced byoverlapping two or more sub-scanning areas. Details regarding obtaininga plurality of sub-images can be found in Chinese Application201410182257.3, the contents of which are hereby incorporated byreference.

The sub-images of the sub-scanning areas may be combined to form animage of the area of interest. The method for combining the sub-imagesmay be found in, for example, U.S. Pre-Grant Publication No.2017-0084025, entitled “SYSTEM AND METHOD FOR IMAGE RECONSTRUCTION”,filed on Aug. 2, 2016, the entire contents of which are herebyincorporated by reference.

FIG. 10 is a block diagram of a data processing module 220 according tosome embodiments of the present disclosure. As illustrated in FIG. 10,the data processing module 220 may include a processor 1010, acalculation unit 1020, an image generator 1030, a storage 1040, and afilter 1050.

The processor 1010 may process the data transmitted from the dataacquisition module 210, or retrieved from a storage medium. In someembodiments, the processed data may be CT data, MR data, PET data, orany combination thereof. In some embodiments, the processer 1010 mayprocess a CT data or MR data to generate an attenuation image, which maybe stored in the storage 1040 or transmitted to the calculation unit1020.

The calculation unit 1020 may be configured to perform calculations ondata (e.g., emission data in a PET/CT system) or images (e.g., anattenuation image). In some embodiments, the calculation unit 1020 mayperform forward-projection on an attenuation image to obtain anattenuation sinogram.

The image generator 1030 may be configured to generate images based ondata processed by the processor 1010 or the calculation unit 1020. Insome embodiments, the image generator 1030 may generate an initial image(e.g., the first PET image Img1 in step 502). In some embodiments, theimage generator may be configured to generate an attenuation sinogramthat is a four-dimensional image with attenuation data (e.g. attenuationvalues of gamma ray) on it. In some embodiments, the attenuationsinogram may be generated by forward-projecting an attenuation image. Insome embodiments, the image generator 1030 may generate an image bycombining a plurality of sub-images as illustrated in FIG. 9 and thedescription thereof.

The storage 1040 may be configured to store the data that transmitted bythe calculation unit 1020, the processor 1010, the calculation unit1020, the image generator 1030, and/or other storage medium, such as,the storage module 240 of the system, etc. Merely by way of example, thestorage 1040 may acquire CT and/or MR data from the data acquisitionmodule 210, and transmit the acquired data to the processor 1010. Asanother example, the storage 1040 may acquire the attenuation image fromthe processor 1010 and transmit it to the calculation unit 1020.

The filter 1050 may be configured to filter the emission sinogramgenerated by the calculation unit 1020. In some embodiments, the filter1050 may filter the LORs of the emission sinogram with a contour filterthat may represent a contour of the subject to be scanned. For example,a LOR locating outside of region of interest may not be processed duringthe image reconstruction process. In some embodiments, the filteredemission sinogram may be transmitted to the image processing module 230,in which the image reconstruction may take place.

FIG. 11 is a flowchart illustrating a data processing method accordingto some embodiments of the present disclosure.

In step 1102, an attenuation image may be generated by performing a CTscan or an MR scan. In some embodiments, the attenuation image may begenerated by back projecting a sinogram by, for example, a filtered backprojection algorithm. The values of the pixels in the attenuation image,which may also be referred as CT values or attenuation values, mayrepresent radiation (e.g., X-ray) attenuation corresponding to differentparts of the subject being scanned.

In step 1104, an initial image may be generated based on the attenuationimage generated in step 1102. The initial image of the subject maycorrespond to an emission sinogram relating to a PET scan. In someembodiments, since the field of view (FOV) of a CT scanner or an MRscanner may be smaller than the FOV of a PET scanner, the initial imagemay be generated by extending the attenuation image of CT or MRspatially to fit the size of a PET image, and thus the initial image mayfit an emission sinogram relating to a CT scan and/or MR scan. In someembodiments, the initial image may include the attenuation value ofgamma ray (e.g., as needed in a PET system) converted from theattenuation value of X-ray (e.g., as needed in a CT/MR system) includedin the attenuation image.

In step 1106, an attenuation sinogram may be generated based on theattenuation image. In some embodiments, the attenuation sinogram may begenerated by performing a forward projection on the attenuation image.Merely by way of example, the attenuation image may be the first CTimage illustrated elsewhere in the present disclosure, and theattenuation sinogram may be generated by forward projecting the first CTimage. In some embodiments, the attenuation sinogram may be afour-dimensional image, while the attenuation image may be athree-dimensional image. Merely by way of example, the three-dimensionalspace of the attenuation image may be an x-y-z space, where z is therotation axis of the detectors, x is the horizontal direction and y isthe vertical direction. The four-dimensional space of the attenuationsinogram may be an s-φ-z-θ space. To better understand thefour-dimensional space, imaging an LOR L′ detected by two detector cellsd_(a) and d_(b), s may represent the distance between O and projectionof L′ on x-y plane, where O is the origin or circle center of thedetector ring, φ may represent the angle between L′ and the positive yaxis, θ may represent the angle between L′ and the x-y plane.

In step 1108, a contour filter may be generated based on the attenuationsinogram generated in step 1106. In some embodiments, the attenuationsinogram may include one or more attenuation values, and the attenuationvalues that are no less than a threshold may be collected to determinean outline of the subject. Furthermore, the contour filter may bedetermined based on the outline of the subject. In some embodiments, thethreshold may be set by an operator, such as, a doctor, an imagingtechnician, an engineer, etc. In some embodiments, the threshold may bea default setting of the system that may be retrieved from a storagemedium of the system or accessible from the system.

It shall be noted that those skilled in the art may realize thatdifferent organs and tissues of the subject may have differentattenuation capacities due to their different capacities to absorbradiation (e.g., X-ray). A tissue with a higher density may absorb moreradiation (e.g., X-rays) than a tissue with a lower density. Forexample, a bone may absorb more X-rays than a lung or another softtissue including, for example, ligaments, muscle, cartilage, tendons,etc. Thus, an attenuation threshold set to determine a contour of thesubject (or the contour filter) on the attenuation sinogram maycorrespond to the specific tissue being scanned. In some embodiments,the attenuation threshold may range between that of air and water.

After the attenuation threshold is set, the contour filter may bedetermined based on the attenuation sinogram. For instance, if thescanner scans the subject from the external part of FOV to the centralFOV, i.e., along the s axis, the attenuation values of the external partof FOV may be less than the attenuation threshold due to the fact thatmost part of the external FOV may be air. Merely by way of example, thecontour filter may be determined based on the attenuation sinogramgenerated by, for example, forward projecting the first CT image.

In step 1110, an emission sinogram may be generated based on PET dataobtained by performing a PET scan. In some embodiments, the emissiondata, such as a LOR, may be the emission sinogram with reference to thes coordinates and φ coordinate of the detected LORs. In someembodiments, the emission data may be the first data related to thefirst PET image illustrated elsewhere in the present disclosure.

In step 1112, the emission sinogram generated in step 1110 may befiltered by the contour filter. The emission data within the scope ofthe contour filter may be acquired from the emission data recorded onthe emission sinogram. In some embodiments, the acquired emission datamay include the LORs corresponding to events which may denote thedetection of two photons emitted from an annihilated point. Merely byway of example, the emission data may be the first data related to thefirst PET image Img1 illustrated elsewhere in the present disclosure.The first data may be filtered by the contour filter to obtain afiltered first data. Details regarding filtering by a contour filter canbe found in Chinese Application 201310004470.0, the contents of whichare hereby incorporated by reference.

It should be noted here that both the attenuation sinogram and theemission sinogram may be four-dimensional. In some embodiments, thedetectors in the CT or MR system are arranged in the same way as in aPET system, such that the points in the attenuation sinogram and theemission sinogram may correspond to each other rigidly. As a result, thecontour filter of the attenuation sinogram may be used to filter theLORs on the emission sinogram.

In step 1114, a PET image may be generated based on the filteredemission sinogram and the initial image. In some embodiments, the PETimage may be reconstructed based on the emission data filtered out bythe contour filter. Merely by way of example, the PET image generated instep 1114 may be the first PET image illustrated elsewhere in thepresent disclosure. The first PET image may be generated based on thefiltered first data.

EXAMPLES

The following examples are provided for illustration purposes, and notintended to limit the scope of the present disclosure.

Example 1

FIG. 12A illustrates an exemplary first CT image, FIG. 12B illustratesan exemplary first PET image corresponding to the first CT imageaccording to some embodiments of the present disclosure. In someembodiments, the first CT image and first PET image was generated byperforming a PET-CT scan. FIG. 12C illustrates an exemplary second CTimage calibrated by the motion field determined by the first PET imageand the second PET image. FIG. 12D illustrates an exemplary second PETimage that was obtained based on a second PET scan performed at adifferent time compared to the first PET scan. FIG. 12E illustrates anexemplary calibrated second PET image. The calibrated second PET imagewas obtained according to the procedure illustrated in FIG. 5 and thedescription thereof. FIG. 12F illustrates an exemplary motion-filedcalibrated PET image that was obtained by way of calibration of thefirst PET image. The calibration was based on the motion field betweenFIG. 12B and FIG. 12 D. Referring to FIG. 12E and FIG. 12B, the twoimages have essentially the same quality. The results may suggest that aPET image with essentially the same quality may be generated by way ofcalibration with a second CT image when the second CT image is generatedbased on the motion-filed calibrated first CT image as described in thepresent disclosure, compared with when the second CT image is acquiredby a conventional CT scan. The images in FIG. 12E and FIG. 12F were bothcalibrated with respect to the motion field.

Example 2

FIG. 13A-FIG. 13F illustrate an exemplary registration result betweenPET images according to some embodiments of the present disclosure. FIG.13A illustrates a PET image reconstructed from data acquired at a firsttime point without attenuation correction. FIG. 13B illustrates a PETimage reconstructed from data acquired at a second time point withoutattenuation correction. FIG. 13C illustrates an attenuation image basedon CT data acquired at the first time point. FIG. 13D illustrates anestimated attenuation image corresponding to the second time pointacquired by the methods described according to some embodiments of thepresent disclosure. FIG. 13E illustrates a differential image betweenthe estimated attenuation image and an actual attenuation image fromdata acquired at the second time point. FIG. 13F illustrates adifferential image between another estimated attenuation image accordingto a traditional method and the actual attenuation image. It can be seenfrom FIG. 13E that, compared to FIG. 13 F, a smaller difference from theactual attenuation image was achieved by the method described in thepresent disclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties such as molecular weight, reaction conditions, and so forth,used to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1-20. (canceled)
 21. A method for image calibration, the methodcomprising: obtaining a first image based on first data relating to asubject; obtaining a second image based on second data relating to thesubject; obtaining a third image of the subject; registering the firstimage and the second image to obtain a motion field; calibrating thethird image based on the motion field to obtain a calibrated image; andcorrecting the second image based on the calibrated image to generate acorrected second image, wherein obtaining a first image based on firstdata relating to a subject further comprising: generating an attenuationsinogram by performing forward projection on the third image; generatinga contour filter based on the attenuation sinogram; filtering the firstdata based on the contour filter to obtain filtered first data; andreconstructing the first image based on the filtered first data.
 22. Themethod of claim 21, the correcting the second image based on thecalibrated image to generate the corrected second image furthercomprising: obtaining an attenuation correction coefficient based on thecalibrated image; and correcting the second image based on theattenuation correction coefficient.
 23. The method of claim 21, thefirst image or the second image including a positron emission tomography(PET) image, and the first image further including: a PET imagecorrected for attenuation correction based on the third image.
 24. Themethod of claim 21, the third image including a computed tomography (CT)image or a magnetic resonance (MR) image.
 25. The method of claim 21,the obtaining a second image based on second data comprising: obtainingthe second data; initializing an attenuation image; sequentiallyupdating the second image and the attenuation image based on the seconddata according to maximum likelihood expectation maximization or maximumlikelihood estimation of attenuation and activity.
 26. The method ofclaim 21, wherein the motion field includes displacement of pixels inthe second image compared to corresponding pixels in the first image.27. The method of claim 21 further comprising: obtaining an attenuationcorrection coefficient based on the calibrated image; and furthercorrecting the corrected second image based on a scatter correctioncoefficient relating to the attenuation correction coefficient.
 28. Themethod of claim 21, the generating a contour filter based on theattenuation sinogram comprising: determining an outline of the subjectin the attenuation sinogram, attenuation values in the outline of thesubject being no less than a threshold; and determining the contourfilter based on the outline of the subject in the attenuation sinogram.29. The method of claim 28, wherein the threshold is determined from arange between an attenuation value of air and an attenuation value ofwater.
 30. The method of claim 21, the filtering the first data based onthe contour filter to obtain filtered first data comprising: generatinga emission sinogram including emission data based on the first data;determining emission data within the scope of the contour filter as thefiltered first data.
 31. The method of claim 21, wherein obtaining asecond image based on second data relating to the subject furthercomprising: filtering the second data based on the contour filter toobtain filtered second data; and reconstructing the second image basedon the filtered second data.
 32. The method of claim 21, the obtaining afirst image based on first data relating to a subject furthercomprising: generating the first image by combining a plurality ofsub-images, wherein the plurality of sub-images are generated byperforming a plurality of sub-scans on the subject.
 33. A method forimage calibration, the method comprising: obtaining a first image basedon first data relating to a subject; obtaining an initial second imagebased on second data relating to the subject; obtaining a third image ofthe subject; during each of a plurality of iterations, obtaining theinitial second image or an updated second image from a prior iteration;registering the second image and the first image to obtain a motionfield; calibrating the third image based on the motion field to obtainan attenuation correction coefficient; obtaining a scatter correctioncoefficient based on the attenuation correction coefficient; andgenerating the updated second image to be used in a next iteration bycorrecting the second image based on the scatter correction coefficient;and reconstructing a corrected second image based on the scattercorrection coefficient and the attenuation correction coefficientobtained in the last iteration of the plurality of iterations, whereinobtaining a first image based on first data relating to a subjectfurther comprising: generating an attenuation sinogram by performingforward projection on the third image; generating a contour filter basedon the attenuation sinogram; filtering the first data based on thecontour filter to obtain filtered first data; and reconstructing thefirst image based on the filtered first data.
 34. A system for imagecalibration, the system comprising: a non-transitory computer-readablestorage medium storing executable instructions, and at least oneprocessor in communication with the computer-readable storage medium,when executing the executable instructions, causing the system toimplement a method comprising: obtaining a first image based on firstdata relating to a subject, a second image based on second data relatingto the subject, and a third image of the subject; registering the firstimage and the second image to obtain a motion field; calibrating thethird image based on the motion field to obtain a calibrated image; andcorrecting the second image based on the calibrated image to generate acorrected second image, wherein obtaining a first image based on firstdata relating to a subject further comprising: generating an attenuationsinogram by performing forward projection on the third image; generatinga contour filter based on the attenuation sinogram; filtering the firstdata based on the contour filter to obtain filtered first data; andreconstructing the first image based on the filtered first data.
 35. Thesystem of claim 34, wherein the system is caused to implement the methodfurther including: obtaining the second data; initializing anattenuation image; and sequentially updating the second image and theattenuation image based on the second data according to maximumlikelihood expectation maximization or maximum likelihood estimation ofattenuation and activity.
 36. The system of claim 35, wherein theupdating the second image and the attenuation image includesreconstructing the second image and the attenuation image according toordered subset expectation maximization.
 37. The system of claim 34,wherein the system is caused to implement the method further comprising:generating the first image by combining a plurality of sub-images,wherein the plurality of sub-images are generated by performing aplurality of sub-scans on the subject.
 38. The system of claim 34,wherein the system is caused to implement the method further comprising:determining an outline of the subject in the attenuation sinogram,attenuation values in the outline of the subject being no less than athreshold; and determining the contour filter based on the outline ofthe subject in the attenuation sinogram.
 39. The method of claim 38,wherein the threshold is determined from a range between an attenuationvalue of air and an attenuation value of water.
 40. The system of claim34, wherein the system is caused to implement the method furthercomprising: generating a emission sinogram including emission data basedon the first data; determining emission data within the scope of thecontour filter as the filtered first data.